Data Science Tutorials

A practical guide to Claude Code Agent Teams, covering how multiple specialized agents share a task list, coordinate through a team lead, and parallelize backend, frontend, database, and documentation work on a single project.

Everything Claude Code (ECC) is an open-source framework that wraps Claude Code with reusable skills, specialized agents, persistent memory, and MCP integrations.

Learn how to use the Seedance 2 API with this step-by-step Python tutorial. Master text-to-video, image interpolation, and multimodal inputs via BytePlus Ark.

Learn how to run Fable 5-Enhanced Gemma 4 locally with prebuilt llama.cpp binaries on an RTX 5070 Ti, test MTP speculative decoding, connect Pi for private AI coding-agent workflows, and troubleshoot common setup issues.

Learn how to run Kimi K2.7 Code locally in five minutes with the prebuilt llama.cpp binary on four RTX PRO 6000 GPUs, then use its web UI and Pi coding agent through an OpenAI-compatible API.

Compare stock market data APIs for backtesting, dashboards, screeners, and AI agents, with Python examples covering splits, reliability, and fundamentals.

Learn how to run MiniMax M3 locally on two RTX PRO 6000 GPUs with llama.cpp, test its OpenAI-compatible API and web UI, and connect it to Pi Coding Agent for private, high-speed local coding workflows.

Learn how to use the built-in Claude Code slash commands to manage context, changes, and costs across sessions, and create custom commands to automate repetitive workflows.

Learn how to use Cofounder 2 to turn a rough idea into a structured company with a business plan, brand kit, engineering tasks, marketing campaigns, and sales workflows through specialized AI agents.

How random forest regression works, where it fails, and how to evaluate, tune, and interpret it. Includes a Python implementation and model comparison framework.

Learn how Claude Code routines run your coding agent in the cloud on a schedule or a GitHub event, so PR reviews and audits finish with your laptop closed.

GGUF packages model weights, tokenizer data, and metadata into a single portable file. Learn how to choose the right quantization level and get started with Ollama.